Relevant bibliographies by topics / Non-parametric learning

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Academic literature on the topic 'Non-parametric learning'

Author: Grafiati
Published: 25 May 2024
Last updated: 31 July 2025

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Journal articles on the topic "Non-parametric learning"

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Liu, Bing, Shi-Xiong Xia, and Yong Zhou. "Unsupervised non-parametric kernel learning algorithm." Knowledge-Based Systems 44 (May 2013): 1–9. http://dx.doi.org/10.1016/j.knosys.2012.12.008.

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Esser, Pascal, Maximilian Fleissner, and Debarghya Ghoshdastidar. "Non-parametric Representation Learning with Kernels." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 11 (2024): 11910–18. http://dx.doi.org/10.1609/aaai.v38i11.29077.

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Unsupervised and self-supervised representation learning has become popular in recent years for learning useful features from unlabelled data. Representation learning has been mostly developed in the neural network literature, and other models for representation learning are surprisingly unexplored. In this work, we introduce and analyze several kernel-based representation learning approaches: Firstly, we define two kernel Self-Supervised Learning (SSL) models using contrastive loss functions and secondly, a Kernel Autoencoder (AE) model based on the idea of embedding and reconstructing data.
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Cruz, David Luviano, Francesco José García Luna, and Luis Asunción Pérez Domínguez. "Multiagent reinforcement learning using Non-Parametric Approximation." Respuestas 23, no. 2 (2018): 53–61. http://dx.doi.org/10.22463/0122820x.1738.

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This paper presents a hybrid control proposal for multi-agent systems, where the advantages of the reinforcement learning and nonparametric functions are exploited. A modified version of the Q-learning algorithm is used which will provide data training for a Kernel, this approach will provide a sub optimal set of actions to be used by the agents. The proposed algorithm is experimentally tested in a path generation task in an unknown environment for mobile robots.
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Khadse, Vijay M., Parikshit Narendra Mahalle, and Gitanjali R. Shinde. "Statistical Study of Machine Learning Algorithms Using Parametric and Non-Parametric Tests." International Journal of Ambient Computing and Intelligence 11, no. 3 (2020): 80–105. http://dx.doi.org/10.4018/ijaci.2020070105.

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The emerging area of the internet of things (IoT) generates a large amount of data from IoT applications such as health care, smart cities, etc. This data needs to be analyzed in order to derive useful inferences. Machine learning (ML) plays a significant role in analyzing such data. It becomes difficult to select optimal algorithm from the available set of algorithms/classifiers to obtain best results. The performance of algorithms differs when applied to datasets from different application domains. In learning, it is difficult to understand if the difference in performance is real or due to
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Yoa, Seungdong, Jinyoung Park, and Hyunwoo J. Kim. "Learning Non-Parametric Surrogate Losses With Correlated Gradients." IEEE Access 9 (2021): 141199–209. http://dx.doi.org/10.1109/access.2021.3120092.

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Rutkowski, Leszek. "Non-parametric learning algorithms in time-varying environments." Signal Processing 18, no. 2 (1989): 129–37. http://dx.doi.org/10.1016/0165-1684(89)90045-5.

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Liu, Mingming, Bing Liu, Chen Zhang, and Wei Sun. "Embedded non-parametric kernel learning for kernel clustering." Multidimensional Systems and Signal Processing 28, no. 4 (2016): 1697–715. http://dx.doi.org/10.1007/s11045-016-0440-1.

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Chen, Changyou, Junping Zhang, Xuefang He, and Zhi-Hua Zhou. "Non-Parametric Kernel Learning with robust pairwise constraints." International Journal of Machine Learning and Cybernetics 3, no. 2 (2011): 83–96. http://dx.doi.org/10.1007/s13042-011-0048-6.

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Kaur, Navdeep, Gautam Kunapuli, and Sriraam Natarajan. "Non-parametric learning of lifted Restricted Boltzmann Machines." International Journal of Approximate Reasoning 120 (May 2020): 33–47. http://dx.doi.org/10.1016/j.ijar.2020.01.003.

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Wang, Mingyang, Zhenshan Bing, Xiangtong Yao, et al. "Meta-Reinforcement Learning Based on Self-Supervised Task Representation Learning." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 8 (2023): 10157–65. http://dx.doi.org/10.1609/aaai.v37i8.26210.

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Meta-reinforcement learning enables artificial agents to learn from related training tasks and adapt to new tasks efficiently with minimal interaction data. However, most existing research is still limited to narrow task distributions that are parametric and stationary, and does not consider out-of-distribution tasks during the evaluation, thus, restricting its application. In this paper, we propose MoSS, a context-based Meta-reinforcement learning algorithm based on Self-Supervised task representation learning to address this challenge. We extend meta-RL to broad non-parametric task distribut
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Dissertations / Theses on the topic "Non-parametric learning"

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Zewdie, Dawit (Dawit Habtamu). "Representation discovery in non-parametric reinforcement learning." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/91883.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2014.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 71-73).<br>Recent years have seen a surge of interest in non-parametric reinforcement learning. There are now practical non-parametric algorithms that use kernel regression to approximate value functions. The correctness guarantees of kernel regression require that the underlying value function be smooth. Most problems of interest do not satisfy this requirement in their native space, but
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Campanholo, Guizilini Vitor. "Non-Parametric Learning for Monocular Visual Odometry." Thesis, The University of Sydney, 2013. http://hdl.handle.net/2123/9903.

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This thesis addresses the problem of incremental localization from visual information, a scenario commonly known as visual odometry. Current visual odometry algorithms are heavily dependent on camera calibration, using a pre-established geometric model to provide the transformation between input (optical flow estimates) and output (vehicle motion estimates) information. A novel approach to visual odometry is proposed in this thesis where the need for camera calibration, or even for a geometric model, is circumvented by the use of machine learning principles and techniques. A non-parametric Bay
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Bratières, Sébastien. "Non-parametric Bayesian models for structured output prediction." Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/274973.

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Structured output prediction is a machine learning tasks in which an input object is not just assigned a single class, as in classification, but multiple, interdependent labels. This means that the presence or value of a given label affects the other labels, for instance in text labelling problems, where output labels are applied to each word, and their interdependencies must be modelled. Non-parametric Bayesian (NPB) techniques are probabilistic modelling techniques which have the interesting property of allowing model capacity to grow, in a controllable way, with data complexity, while maint
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Prando, Giulia. "Non-Parametric Bayesian Methods for Linear System Identification." Doctoral thesis, Università degli studi di Padova, 2017. http://hdl.handle.net/11577/3426195.

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Recent contributions have tackled the linear system identification problem by means of non-parametric Bayesian methods, which are built on largely adopted machine learning techniques, such as Gaussian Process regression and kernel-based regularized regression. Following the Bayesian paradigm, these procedures treat the impulse response of the system to be estimated as the realization of a Gaussian process. Typically, a Gaussian prior accounting for stability and smoothness of the impulse response is postulated, as a function of some parameters (called hyper-parameters in the Bayesian framework
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Angola, Enrique. "Novelty Detection Of Machinery Using A Non-Parametric Machine Learning Approach." ScholarWorks @ UVM, 2018. https://scholarworks.uvm.edu/graddis/923.

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A novelty detection algorithm inspired by human audio pattern recognition is conceptualized and experimentally tested. This anomaly detection technique can be used to monitor the health of a machine or could also be coupled with a current state of the art system to enhance its fault detection capabilities. Time-domain data obtained from a microphone is processed by applying a short-time FFT, which returns time-frequency patterns. Such patterns are fed to a machine learning algorithm, which is designed to detect novel signals and identify windows in the frequency domain where such novelties occ
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Bartcus, Marius. "Bayesian non-parametric parsimonious mixtures for model-based clustering." Thesis, Toulon, 2015. http://www.theses.fr/2015TOUL0010/document.

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Cette thèse porte sur l’apprentissage statistique et l’analyse de données multi-dimensionnelles. Elle se focalise particulièrement sur l’apprentissage non supervisé de modèles génératifs pour la classification automatique. Nous étudions les modèles de mélanges Gaussians, aussi bien dans le contexte d’estimation par maximum de vraisemblance via l’algorithme EM, que dans le contexte Bayésien d’estimation par Maximum A Posteriori via des techniques d’échantillonnage par Monte Carlo. Nous considérons principalement les modèles de mélange parcimonieux qui reposent sur une décomposition spectrale de
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Mahler, Nicolas. "Machine learning methods for discrete multi-scale fows : application to finance." Phd thesis, École normale supérieure de Cachan - ENS Cachan, 2012. http://tel.archives-ouvertes.fr/tel-00749717.

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This research work studies the problem of identifying and predicting the trends of a single financial target variable in a multivariate setting. The machine learning point of view on this problem is presented in chapter I. The efficient market hypothesis, which stands in contradiction with the objective of trend prediction, is first recalled. The different schools of thought in market analysis, which disagree to some extent with the efficient market hypothesis, are reviewed as well. The tenets of the fundamental analysis, the technical analysis and the quantitative analysis are made explicit.
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GONÇALVES, JÚNIOR Paulo Mauricio. "Multivariate non-parametric statistical tests to reuse classifiers in recurring concept drifting environments." Universidade Federal de Pernambuco, 2013. https://repositorio.ufpe.br/handle/123456789/12226.

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Data streams are a recent processing model where data arrive continuously, in large quantities, at high speeds, so that they must be processed on-line. Besides that, several private and public institutions store large amounts of data that also must be processed. Traditional batch classi ers are not well suited to handle huge amounts of data for basically two reasons. First, they usually read the available data several times until convergence, which is impractical in this scenario. Second, they imply that the context represented by data is stable in time, which may not be true. In fact, t
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Gonçalves, Júnior Paulo Mauricio. "Multivariate non-parametric statistical tests to reuse classifiers in recurring concept drifting environments." Universidade Federal de Pernambuco, 2013. https://repositorio.ufpe.br/handle/123456789/12288.

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Data streams are a recent processing model where data arrive continuously, in large quantities, at high speeds, so that they must be processed on-line. Besides that, several private and public institutions store large amounts of data that also must be processed. Traditional batch classi ers are not well suited to handle huge amounts of data for basically two reasons. First, they usually read the available data several times until convergence, which is impractical in this scenario. Second, they imply that the context represented by data is stable in time, which may not be true. In fact, t
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Wei, Wei. "Probabilistic Models of Topics and Social Events." Research Showcase @ CMU, 2016. http://repository.cmu.edu/dissertations/941.

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Structured probabilistic inference has shown to be useful in modeling complex latent structures of data. One successful way in which this technique has been applied is in the discovery of latent topical structures of text data, which is usually referred to as topic modeling. With the recent popularity of mobile devices and social networking, we can now easily acquire text data attached to meta information, such as geo-spatial coordinates and time stamps. This metadata can provide rich and accurate information that is helpful in answering many research questions related to spatial and temporal
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Books on the topic "Non-parametric learning"

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Carús, Pablo. Introdução às metodologias da investigação em motricidade humana. Manual prático de análises de dados com SPSS. Imprensa Universidade de Évora, 2020. http://dx.doi.org/10.24902/uevora.26.

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The Statistical Package for the Social Sciences (SPSS) is a computer statistical program used to perform various types of statistical analyses. This book aims to teach how to use SPSS, in an applied way in the introduction to research methodologies in human motricity. To do so, we start by teaching how to prepare a data file in SPSS and then by explaining how the transformation of new variables is carried out and the conditions of applicability of parametric tests. Finally, we attend to the learning procedure of computation and interpretation of the window of results of some parametric and non
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Book chapters on the topic "Non-parametric learning"

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Webb, Geoffrey I., Eamonn Keogh, Risto Miikkulainen, Risto Miikkulainen, and Michele Sebag. "Non-Parametric Methods." In Encyclopedia of Machine Learning. Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_598.

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Krishnan, N. M. Anoop, Hariprasad Kodamana, and Ravinder Bhattoo. "Non-parametric Methods for Regression." In Machine Learning for Materials Discovery. Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-44622-1_5.

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Szörényi, Balázs, Snir Cohen, and Shie Mannor. "Non-parametric Online AUC Maximization." In Machine Learning and Knowledge Discovery in Databases. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-71246-8_35.

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Nguyen, Hoang-Vu, and Jilles Vreeken. "Non-parametric Jensen-Shannon Divergence." In Machine Learning and Knowledge Discovery in Databases. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23525-7_11.

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Hino, Hideitsu, and Noboru Murata. "A Non-parametric Maximum Entropy Clustering." In Artificial Neural Networks and Machine Learning – ICANN 2014. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11179-7_15.

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Koronakos, Gregory, and Dionisios N. Sotiropoulos. "Non-parametric Performance Measurement with Artificial Neural Networks." In Learning and Analytics in Intelligent Systems. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-49724-8_14.

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Chen, Chun-Sheng, Christoph F. Eick, and Nouhad J. Rizk. "Mining Spatial Trajectories Using Non-parametric Density Functions." In Machine Learning and Data Mining in Pattern Recognition. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23199-5_37.

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Karlinsky, Leonid, and Shimon Ullman. "Using Linking Features in Learning Non-parametric Part Models." In Computer Vision – ECCV 2012. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33712-3_24.

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Rutkowski, Leszek, Maciej Jaworski, and Piotr Duda. "General Non-parametric Learning Procedure for Tracking Concept Drift." In Studies in Big Data. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-13962-9_9.

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Babagholami-Mohamadabadi, Behnam, Seyed Mahdi Roostaiyan, Ali Zarghami, and Mahdieh Soleymani Baghshah. "Multi-Modal Distance Metric Learning: ABayesian Non-parametric Approach." In Computer Vision - ECCV 2014 Workshops. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16199-0_5.

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Conference papers on the topic "Non-parametric learning"

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Gan, Yun, Qingjie Liu, Hongrui Chen, Muhua Zhang, Shuang Wei, and Na Qin. "Non-parametric Dynamic Point Cloud Segmentation System Based on Time Series and Spatial Clustering." In 2024 IEEE 13th Data Driven Control and Learning Systems Conference (DDCLS). IEEE, 2024. http://dx.doi.org/10.1109/ddcls61622.2024.10606903.

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González-Santiago, Jonathan, Wolfgang Gross, and Wolfgang Middelmann. "Evaluation of Self-Supervised Learning Techniques for Non-Parametric Few-Shot Hyperspectral-Lidar Classification." In 2024 14th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS). IEEE, 2024. https://doi.org/10.1109/whispers65427.2024.10876496.

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Nandhini, K., and Anup Aprem. "Bayesian Non-Parametric Active Learning for Information Asymmetric ECCM in Cognitive Radar Electronic Warfare." In 2025 IEEE International Radar Conference (RADAR). IEEE, 2025. https://doi.org/10.1109/radar52380.2025.11031745.

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Sturrock, C. P., and W. F. Bogaerts. "Computer Learning Systems in Corrosion." In CORROSION 1996. NACE International, 1996. https://doi.org/10.5006/c1996-96657.

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Abstract A collection of data documenting the stress corrosion cracking (SCC) behavior of austenitic stainless steels provides the basis for an automated learning system. Computer learning systems based on classical and non-parametric statistics, connectionist models, machine learning methods, and fuzzy logic are described. An original method for inducing fuzzy rules from input-output data is presented. All of these computer learning systems are used to solve a typical problem of corrosion engineering: determine the likelihood of SCC of austenitic stainless steels given varying conditions of t
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Herrera-Ruiz, Juan Federico, Javier Fontalvo, and Oscar Andr�s Prado-Rubio. "Hybrid model development for Succinic Acid fermentation: relevance of ensemble learning for enhancing model prediction." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.153338.

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Sustainable development goals have spurred advancements in bioprocess design, driven by improved process monitoring, data storage, and computational power. High-fidelity models are essential for advanced process system engineering, yet accurate parametric models for bioprocessing remain challenging due to overparameterization, often resulting in poor predictive accuracy. Hybrid modeling, combining parametric and non-parametric methods, offers a promising solution by enhancing accuracy while maintaining interpretability. This study explores hybrid models for succinic acid fermentation by Escher
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Hutchinson, Brian, and Jasha Droppo. "Learning non-parametric models of pronunciation." In ICASSP 2011 - 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2011. http://dx.doi.org/10.1109/icassp.2011.5947455.

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Vaandrager, Maarten, Robert Babuska, Lucian Busoniu, and Gabriel A. D. Lopes. "Imitation learning with non-parametric regression." In 2012 IEEE International Conference on Automation, Quality and Testing, Robotics (AQTR 2012). IEEE, 2012. http://dx.doi.org/10.1109/aqtr.2012.6237681.

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Silva-Lugo, Jose, Laura Warner, and Sebastian Galindo. "FROM PARAMETRIC TO NON-PARAMETRIC STATISTICS IN EDUCATION AND AGRICULTURAL EDUCATION RESEARCH." In 14th International Conference on Education and New Learning Technologies. IATED, 2022. http://dx.doi.org/10.21125/edulearn.2022.0841.

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Kamath, Sudeep, Alon Orlitsky, Venkatadheeraj Pichapati, and Ehsan Zobeidi. "On Learning Parametric Non-Smooth Continuous Distributions." In 2020 IEEE International Symposium on Information Theory (ISIT). IEEE, 2020. http://dx.doi.org/10.1109/isit44484.2020.9174474.

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Baldwin, I., and P. Newman. "Non-parametric learning for natural plan generation." In 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2010). IEEE, 2010. http://dx.doi.org/10.1109/iros.2010.5651569.

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Reports on the topic "Non-parametric learning"

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Rodriguez, Fernando, and Guillermo Sapiro. Sparse Representations for Image Classification: Learning Discriminative and Reconstructive Non-Parametric Dictionaries. Defense Technical Information Center, 2008. http://dx.doi.org/10.21236/ada513220.

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Engel, Bernard, Yael Edan, James Simon, Hanoch Pasternak, and Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, 1996. http://dx.doi.org/10.32747/1996.7613033.bard.

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The objectives of this project were to develop procedures and models, based on neural networks, for quality sorting of agricultural produce. Two research teams, one in Purdue University and the other in Israel, coordinated their research efforts on different aspects of each objective utilizing both melons and tomatoes as case studies. At Purdue: An expert system was developed to measure variances in human grading. Data were acquired from eight sensors: vision, two firmness sensors (destructive and nondestructive), chlorophyll from fluorescence, color sensor, electronic sniffer for odor detecti
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